Abstract B052: Utilizing Dimensionality Reduction for Classification of Cell Senescence and Immune Synapse Formation via Imaging Flow Cytometry
Notice bibliographique
Résumé
Abstract The advent of imaging flow cytometry has become a key area of growth for the analysis of cellular process by integrating the dimensionality, resolution, and throughput of flow cytometry with spatial information from image data. Flow cytometry is a single cell method for the characterization of cells or particles in a suspension. Unsupervised machine learning applied to high-parameter flow cytometry datasets allows for an unbiased exploration of complex, unresolved cell populations compared to manual gating. However, these methodologies have not been significantly applied to imaging-derived parameters from imaging flow cytometry. This study investigates dimensionality reduction and clustering for classifying two key cell populations from two assays: the SPIDER B-gal fluorescent assay for detecting cell senescence in fibroblasts and a co-culture assay evaluating synapse formation between engineered CAR T-cells and tumor cell lines. Data were obtained using the FACSDiscover S8 imaging flow cytometer. Typically, flow cytometry analysis involves gating—setting thresholds on forward scatter (FSC), side scatter (SSC), and fluorescence intensity plots to isolate cell subsets based on size, granularity, and expression. This study utilized these approaches to identify senescent cells and immune synapse aggregates based on morphological image-derived features. The analytical workflow for both datasets included debris and apoptotic cell clean-up gates, parameter scaling, and t-SNE application on light loss, FSC, and SSC parameters. Senescence entails permanent cell division cessation alongside gene and morphology changes and is crucial in aging and tumorigenesis. We hypothesized that senescence-induced morphological changes are observable using imaging parameters alone. Validation involved overlaying SPIDER B-gal+ cells on t-SNE post-gating non-debris singlet events, showing SPIDER B-gal+ clustering on a t-SNE island. A heatmap corroborated imaging features of senescent morphology. Assessing immune synapse formation is vital for evaluating CAR T-cell therapy efficacy, facilitated by imaging flow cytometry for high-throughput synapse assessment. CAR-T cells were co-cultured with target cells under varied conditions and timepoints, then acquired on the instrument. A manual gating strategy identified T-cell and target cell synapse formation using CD45 and target antigen expressions. Manually gated events were overlayed on a t-SNE plot utilizing light loss, FSC, and SSC parameters. Multiple islands correlated with low-order aggregates, further gated for T-cell and target cell synapse events. This approach underscores the potential of imaging-derived parameters for identifying senescence and synapse formation using dimensionality reduction, progressing towards label-free cell population identification. Techniques like Hyperfinder, which auto-generate gating strategies, could then be used to develop unsupervised gating strategies for sorting purposes. Citation Format: Mohamed M. Moustafa, Nicholas Battaglia, Viji Premkumar, Ozzie Civelekoglu, Nikki Heller, Raffaello Cimbro. Utilizing Dimensionality Reduction for Classification of Cell Senescence and Immune Synapse Formation via Imaging Flow Cytometry [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr B052.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».